What’s the difference between short term happy moment and long term happy moment?

HappyDB is a corpus of 100,535 crowd-sourced happy moments. The goal of this project is to look deeper to compare the causes between the short term happy moment and long term happy moment. I apply natural language processing, text mining and data manipulation to derive interesting findings in the collection of happy moments.

## Warning: package 'bindrcpp' was built under R version 3.4.4

Data Summary

Firstly, I check the number of last 24 hours happy moment is 49831 which is very similar with the number of last 3 months happy moment: 50704.

24 hours country frequency

## 
##  USA  IND  CAN       GBR  VEN  PHL  MEX  AUS  NGA 
## 5594  694   42   35   34   34   17   14    8    8

3 months country frequency

## 
##  USA  IND  CAN       VEN  GBR  PHL  AUS  BRA  MEX 
## 6118  708   41   37   37   25   24   12   11   11

24 hours marital frequency

## 
##    single   married  divorced separated   widowed           
##      3461      2718       333        49        42        31

3 months marital frequency

## 
##    single   married  divorced separated   widowed           
##      3845      2878       352        70        44        26

24 hours gender frequency

## 
##    m    f    o      
## 3347 3230   42   15

3 months gender frequency

## 
##    m    f    o      
## 3654 3511   34   16

24 hours parenthood frequency

## 
##    n    y      
## 3926 2691   17

3 months parenthood frequency

## 
##    n    y      
## 4355 2847   13

24 hours age frequency

## 
##  23  21  25  29  19  33  27  35  37  16 
## 224 220 215 205 192 190 183 182 179 174

3 months age frequency

## 
##  23  25  21  33  29  27  19  35  37  22 
## 244 236 232 227 219 216 203 188 181 177

From above frequency tables, we can see that the most frequent class in each category of workers are very similar and share similar number. So it can be concluded that comparison on short term happy moment and long term happy moment is not affected by country, marital, gender, parenthood and age. So next, we can begin to compare.

Comparison on Happiness category label

The Happiness category label predicted by the author (Please see the reference for details) which is 7 categories: achievement, affection, bonding, enjoy_the_moment, exercise, leisure, nature. Below is the percentage of each category appears in the collection of the last 24 hours happy moment as well as in the collection of the last 3 months happy moment.

24 hours happy moment Happiness category label

## 
##      achievement        affection          bonding enjoy_the_moment 
##        30.980715        32.818928        10.477414        13.341093 
##         exercise          leisure           nature 
##         1.531175         8.695390         2.155285

3 months happy moment Happiness category label

## 
##      achievement        affection          bonding enjoy_the_moment 
##       36.5947460       35.1333228       10.8591038        8.8671505 
##         exercise          leisure           nature 
##        0.8658094        6.1632218        1.5166456

From above group barplot and pie charts, we can see that when people talking about their happy moment in last 3 months, they are more focus on achievement, affection and bonding. While, when people talking about their happy moment in last 24 hours, they are more focus on enjoy the moment, exercise, leisure and nature.

Comparison on the most frequent words

24 hours most 20 frequent words

## 
##   friend  watched    night   played     home   dinner  morning     feel 
##     5035     2694     2374     2236     2234     2208     2065     2010 
##   family  enjoyed    found      son     game  finally daughter     nice 
##     1932     1901     1751     1735     1694     1652     1649     1647 
## favorite    hours     life     love 
##     1617     1568     1440     1403

3 months most 20 frequent words

## 
##   friend   family  finally      job     home    found     feel birthday 
##     5857     2760     2270     2162     1977     1969     1936     1934 
##      son     play daughter   bought watching  enjoyed    event     life 
##     1898     1822     1759     1747     1691     1663     1557     1555 
##     love received     game  started 
##     1553     1514     1510     1489

Word cloud

24 hours Word Cloud

3 months Word Cloud

Friend, family, son, daughter are always the most common words in the happy moment both in last 24 hours and last 3 months. So obviously the key to be happy is spending more time with friends and family.

In the last 24 hours dataset, watched, game appear more frequently. Which means game, tv show, film are really a good way to relax people and make them feel happy immediately in daily life. Additionally night, morning are also very common in the last 24 hours. So a good begining and ending of the day can make someone’s day. Moreover dinner is the 6th most frequent words in the last 24 hours dataset, which we can see that dinner is the most important meal in a day and food can make people feel good quickly.

In contrast, event, birthday appear more frequently in the last 3 months. It is resonable because people easily think over and remember some great event and something can change their life when they are asked about the happy moment in a longer period. People needs holiday and event to refesh themselves and get reunion with their family and friends to make them feel great. The other common words are job, bought. It’s also imaginable because job is really important in the life and shopping is usually a way to decompress. And I feel a little surprised to see bought is in the common words of the last 3 month instead of the last 24 hours. This shows shopping is a good way to be relaxed and help people be happy in a long term.

Summary

  1. Spending time with family and friends are the best way to be happy both in the long term and short term.
  2. Watch tv, play game, great food are a quick way to make people happy in a short term.
  3. Event, job, shopping are the methods to keep people happy for a longer term.

References

Akari Asai, Sara Evensen, Behzad Golshan, Alon Halevy, Vivian Li, Andrei Lopatenko, Daniela Stepanov, Yoshihiko Suhara, Wang-Chiew Tan, Yinzhan Xu, ``HappyDB: A Corpus of 100,000 Crowdsourced Happy Moments’’, LREC ’18, May 2018. (to appear)

https://github.com/rit-public/HappyDB